EEG Classification with BSA Spike Encoding Algorithm and Evolving Probabilistic Spiking Neural Network

Nuntalid, Nuttapod; Dhoble, Kshitij; Kasabov, Nikola

doi:10.1007/978-3-642-24955-6_54

Cited by 45 publications

(18 citation statements)

References 15 publications

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“…Schrauwen and Van Campenhout [6] improved algorithm proposed by De Garis et al by optimizing the deconvolution threshold yielding the socalled Bens Spiker Algorithm (BSA). BSA has been used widely as a rate encoding method for ASNN applications [7][8][9]. The major problem of this type of rate encoding is that an averaging time window is required for each sampling of the input signal, which as a consequence limits the temporal resolution of the encoded signals.…”

Section: Introductionmentioning

confidence: 99%

Wavelet decomposition and phase encoding of temporal signals using spiking neurons

2016

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Section: Introductionmentioning

confidence: 99%

Wavelet decomposition and phase encoding of temporal signals using spiking neurons

2016

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“…Software simulations of different brain data will be conducted in the future, including: EEG pattern recognition and comparing results with previous studies of using reservoir computing [57]; EEG pattern recognition for BCI [41,48]; fMRI pattern recognition and comparison with previous studies [58]. A module for dynamic visualisation of NeuCube polychronisation patterns will be developed along with their interpretation in terms of functional and structural pathways discovered.…”

Section: Conclusion and Further Directionsmentioning

confidence: 99%

“…EEG pattern recognition [48] can be directed to practical applications, such as: BCI [31]; classification of epilepsy [22] (e.g., using deSNN); robot control through EEG signals [41] and robot navigation [2] (e.g., using SPAN). In case of BCI, one scenario is when a NeuCube architecture can be evolved and dynamically visualized to represent the learning process of a subject.…”

Section: Applicationsmentioning

confidence: 99%

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NeuCube EvoSpike Architecture for Spatio-temporal Modelling and Pattern Recognition of Brain Signals

Kasabov

2012

Lecture Notes in Computer Science

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Abstract. The brain functions as a spatio-temporal information processing machine and deals extremely well with spatio-temporal data. Spatio-and spectro-temporal data (SSTD) are the most common data collected to measure brain signals and brain activities, along with the recently obtained gene and protein data. Yet, there are no computational models to integrate all these different types of data into a single model to help understand brain processes and for a better brain signal pattern recognition. The EU FP7 Marie Curie IIF EvoSpike project develops methods and tools for spatio and spectro temporal pattern recognition. This paper proposes a new evolving spiking model called NeuCube as part of the EvoSpike project, especially for modeling brain data. The NeuCube is 3D evolving Neurogenetic Brain Cube of spiking neurons that is an approximate map of structural and functional areas of interest of an animal or human brain. Optionally, gene information is included in the NeuCube in the form of gene regulatory networks that relate to spiking neuronal parameters of interest. Different types of brain SSTD can be used to train a NeuCube, including: EEG, fMRI, video-, image-and sound data, complex multimodal data. Potential applications are: EEG -, fMRI-, and multimodal brain data modeling and pattern recognition; Brain-Computer Interfaces; cognitive and emotional robots; neuro-prosthetics and neuro-rehabilitation; modeling brain diseases. Analysis of the internal structure of the model can trigger new hypotheses about spatio-temporal pathways in the brain.

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“…Schrauwen and Van Campenhout [55] improved algorithm proposed by De Garis et al by optimizing the deconvolution threshold yielding the so-called Bens Spiker Algorithm (BSA). BSA has been used widely as a rate encoding method for ASNN applications [56]- [58]. The major problem of this type of rate encoding is that an averaging time window is required for each sampling of the input signal, which as a consequence limits the temporal resolution of the encoded signals.…”

Section: Rate Encodingmentioning

confidence: 99%

System Design and Implementation of a Fast and Accurate Bio-Inspired Spiking Neural Network

Wang¹

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All rights reserved.iv DEDICATION This dissertation is dedicated to the following people:• My wife, Yanyan Pan, who has shared the life uncertainties and challenges during the completion of this dissertation, yet been always patient and supportive of my work.• My parents, Yimin Wang and Wenhua Li, who emphasized the importance of education and always encouraged me even from thousands of miles away.• My son, Lucas Wang, who has grown into a cute one year old boy in spite of his father spending so much time away from him working on this dissertation. Interictal spike (IS) is a type of transient discharge commonly found in the electroencephalography (EEG) records from epilepsy patients. The detection of IS remains an essential task for 3D source localization as well as in developing algorithms for essential in seizure prediction and guided therapy. We present in this work a new IS detection technology method using the phase encoding method with customized wavelet sensor neuron and a specially designed ASNN structure. The detection results confirm the ability of such ASNN to capture IS automatically from multichannel EEG records.

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EEG Classification with BSA Spike Encoding Algorithm and Evolving Probabilistic Spiking Neural Network

Cited by 45 publications

References 15 publications

Wavelet decomposition and phase encoding of temporal signals using spiking neurons

Wavelet decomposition and phase encoding of temporal signals using spiking neurons

NeuCube EvoSpike Architecture for Spatio-temporal Modelling and Pattern Recognition of Brain Signals

System Design and Implementation of a Fast and Accurate Bio-Inspired Spiking Neural Network

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